First, we train the supernet with Gumble Softmax sampling [1]} to get the importance score for the blocks within each \(\texttt {MP}\) , which can be expressed as
\(\small \mathcal {X}_{i+1} = \sum _n \frac{e^{({\alpha }^n_i + \epsilon ^n_i) / \tau }}{\sum _{n} e^{({\alpha }^n_i + \epsilon ^n_i) / \tau }} \cdot \texttt {MP}_{i, j}({\mathcal {X}_i}),\) 
